GEE expression gives none value

Question

I am trying to build a code to estimate evapotranspiration using satellite remote sensing data via google earth engine. This part of the code I couldn't debug, When I try to reduce "tao_sw" I get none value. Although "W", "pres" and "cos_theta" gives valid values.

import ee

# # Trigger the authentication flow.

# # ee.Authenticate()

# Initialize the library.

ee.Initialize()

def reducertest (img, aoi):

    value_r = img.reduceRegion(

    reducer= ee.Reducer.mean(),

    geometry=aoi,

    scale= 1000,

    maxPixels=9e14

    )

    return value_r.getInfo()

# time of the study

start_date = '2018-01-20'

end_date = '2018-01-30'

year, month , _ = start_date.split('-')

print(year,month)

aoi = ee.Geometry.Polygon([

          [

            [

              -120.02153301116874,

              36.55933992273219

            ],

            [

              -120.04941370309581,

              36.419636408472286

            ],

            [

              -119.77773905385078,

              36.415462282458435

            ],

            [

              -119.78033260658853,

              36.5588191094016

            ],

            [

              -120.02153301116874,

              36.55933992273219

            ]

          ]

        ])

# landsat

image = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')\

  .filterBounds(aoi)\

  .filter(ee.Filter.date(start_date, end_date)).first()

# meteorology

time_start = image.get('system:time_start')

meteo_inst_source = 'ECMWF/ERA5_LAND/HOURLY'

DATASET = ee.ImageCollection(meteo_inst_source)

#LINEAR INTERPOLATION

TIME_START_NUM=ee.Number(time_start)

PREVIOUS_TIME=TIME_START_NUM.subtract(3*60*60*1000)

NEXT_TIME=TIME_START_NUM.add(3*60*60*1000)

PREVIOUS_IMAGE=(DATASET.filter(ee.Filter.date(PREVIOUS_TIME,TIME_START_NUM))

                        .limit(1, 'system:time_start', False).first())

NEXT_IMAGE=(DATASET.filter(ee.Filter.date(TIME_START_NUM,NEXT_TIME))

                        .limit(1, 'system:time_start', False).first())

IMAGE_PREVIOUS_TIME= ee.Number(PREVIOUS_IMAGE.get('system:time_start'))

IMAGE_NEXT_TIME=ee.Number(NEXT_IMAGE.get('system:time_start'))

DELTA_TIME=(TIME_START_NUM.subtract(IMAGE_PREVIOUS_TIME)).divide(IMAGE_NEXT_TIME.subtract(IMAGE_PREVIOUS_TIME))

tair_c = NEXT_IMAGE.select('temperature_2m')\

        .subtract(PREVIOUS_IMAGE.select('temperature_2m'))\

        .multiply(DELTA_TIME).add(PREVIOUS_IMAGE.select('temperature_2m'))\

        .rename('AirT_G')

# SATURATED VAPOR PRESSURE [KPA]

esat = tair_c.expression(

    '0.6108 * (exp((17.27 * T_air) / (T_air + 237.3)))', {'T_air': tair_c.subtract(273.15)})

# PRESSURE [PA] CONVERTED TO KPA

tdp = NEXT_IMAGE.select('dewpoint_temperature_2m')\

    .subtract(PREVIOUS_IMAGE.select('dewpoint_temperature_2m'))\

    .multiply(DELTA_TIME).add(PREVIOUS_IMAGE.select('dewpoint_temperature_2m'))\

    .rename('tdp')

# ACTUAL VAPOR PRESSURE [KPA]

ea1 = tdp.expression(

    '0.6108 * (exp((17.27 * T_air) / (T_air + 237.3)))',{

    'T_air': tdp.subtract(273.15)})

# RELATIVE HUMIDITY (%)

rh = ea1.divide(esat).multiply(100).rename('RH_G')

# col_meteorology= get_meteorology(image, time_start)

T_air = tair_c.select('AirT_G')

# Resample

tair_c = tair_c.subtract(273.15).resample('bilinear')

# wind_med = wind_med.resample('bilinear')

rh = rh.resample('bilinear')

# swdown24h = i_Rs_24h.resample('bilinear')

# rn24h = i_Rn_24h.resample('bilinear')

###################################################################

#SRTM DATA ELEVATION

SRTM_ELEVATION ='USGS/SRTMGL1_003'

srtm = ee.Image(SRTM_ELEVATION).clip(aoi)

z_alt = srtm.select('elevation')

###################################################################

#ATMOSPHERIC PRESSURE [KPA]

#SHUTTLEWORTH (2012)

pres = image.expression(

    '101.3 * ((293 - (0.0065 * Z))/ 293) ** 5.26 ', {

    'Z' : z_alt}).rename('P_ATM')

#ACTUAL VAPOR PRESSURE (ea) [KPA]

ea = es.multiply(rh).divide(100).rename('EA')

#SATURATION VAPOR PRESSURE (es) [KPA]

es = image.expression(

    ' 0.6108 *(exp( (17.27 * T_air) / (T_air + 237.3)))', {

    'T_air': T_air}).rename('ES')

#WATER IN THE ATMOSPHERE [mm]

#Garrison and Adler (1990)

W = image.expression(

    '(0.14 * EA * PATM) + 2.1', {

    'PATM' : pres,

    'EA' : ea}).rename('W_ATM')

#SOLAR ZENITH ANGLE OVER A HORZONTAL SURFACE

azimuth_angle = image.get('SUN_AZIMUTH')

SUN_ELEVATION=ee.Number(90).subtract(azimuth_angle)

solar_zenith = ee.Number(90).subtract(SUN_ELEVATION)

degree2radian = 0.01745

solar_zenith_radians = solar_zenith.multiply(degree2radian)

cos_theta = solar_zenith_radians.cos()

#BROAD-BAND ATMOSPHERIC TRANSMISSIVITY (tao_sw)

#ASCE-EWRI (2005)

tao_sw = image.expression(

    '0.35 + 0.627 * exp(((-0.00146 * P)/(Kt * cos_theta)) - (0.075 * (W / cos_theta)**0.4))', {

    'P' : pres,

    'W': W,

    'Kt' : ee.Number(1),

    'cos_theta' : cos_theta}).rename('Tao_sw')

t_w = reducertest(W,aoi) ###################################

t_pres = reducertest(pres,aoi) ###################################

t_cos_theta = cos_theta.getInfo() ###################################

t_tao_sw = reducertest(tao_sw,aoi) ###################################

print(t_tao_sw)

I tried changing the aoi to include the whole image and still got none value